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Structural Reliability theory provides the means for managing uncertainties in modern codified structural design. Yet, historical records show that most structural collapses occur due to uncertainties not explicitly considered in reliability calculations, such as gross errors in design and execution, unanticipated loading, poor understanding of phenomena, new technology, etc. Recently, it has been shown that neglecting the above ‘non-technical’ factors in reliability-based optimisation leads to non-redundant optimal designs, with negative impacts on structural robustness. Surprisingly, the observed collapse frequency of structures was recently found to be smaller than calculated failure probabilities. This reveals that structural reliability calculations are conservative with respect to technical factors, while grossly overlooking non-technical factors. Population wise, the two independent errors may be partially offsetting each other. The discussion herein covers significant ‘open issues’ to be addressed by the Structural Safety community, before risk-based performance metrics can be incorporated into structural design practice. [ABSTRACT FROM AUTHOR]

Often the errors in the measurement of copolymerizations are not accurately determined or included in the calculation of reactivity ratios. Some knowledge of the errors in the initial monomer ratio, conversion, and copolymer composition is however essential to obtain reliable (unbiased) reactivity ratios with a realistic uncertainty. It is shown that the errors serve a trifold purpose; they can serve as weighing factors in the fit, they can be compared with the fit residues to decide whether the chosen model is adequate for the data and they can be used to construct a realistic joint confidence interval for the reactivity ratios. The best approach is to have an estimate of the individual errors in the copolymer composition, either from a thorough error propagation exercise or from replicate measurements. With these errors, the χ2‐joint confidence intervals can then be constructed which gives a realistic estimate of the errors in the reactivity ratios. Utilizing the Errors in Variables Method (EVM) is correct and useful, but only if the individual errors in all the variables in each experiment are more or less known. [ABSTRACT FROM AUTHOR]

Path integration is the navigational process by which animals construct a memory of a previous location by continuously measuring and summing their movements to form a single home vector pointing to the starting location. It is intrinsically error prone, subject to random errors and, potentially, to systematic errors in either measurement or the summing algorithm. Both types of errors lead to an incorrect vector memory and thus to an error in homing. Because the errors are incurred when animals move, they are theoretically predictable from the movements. We analyzed the behavior of fiddler crabs (Leptuca pugilator) as they performed foraging excursions followed by homing with varying degrees of error. From video recordings we measured body orientations and locations and computed these spatiotemporal path characteristics: duration, distance, turns, bearing and arc sector. These were analyzed for their effect on, separately, the magnitude, and the direction, of crabs' homing error. The magnitude of the homing error was predicted by arc sector, Dbearing and path length, and several interactions. The direction of the homing error was predicted by interactions including arc sector x Dbearing, arc sector x turns, and Dbearing x turns. Covariance among these factors results in a path that traces a large arc while maintaining body orientation toward the burrow direction and leads to an error with the same clockwise/counterclockwise sign as the arc and the body turns. These results place L. pugilator's path integration mechanism among others with known systematic errors. [ABSTRACT FROM AUTHOR]

The redshifted 21 cm line signal is a powerful probe of the cosmic dawn and the epoch of reionization. The global spectrum can potentially be detected with a single antenna and spectrometer. However, this measurement requires an extremely accurate calibration of the instrument to facilitate the separation of the 21 cm signal from the much brighter foregrounds and possible variations in the instrument response. Understanding how the measurement errors propagate in a realistic instrument system and affect system calibration is the focus of this work. We simulate a 21 cm global spectrum observation based on the noise wave calibration scheme. We focus on how measurement errors in reflection coefficients affect the noise temperature and how typical errors impact the recovery of the 21 cm signal, especially in the frequency domain. Results show that for our example set up, a typical vector network analyzer (VNA) measurement error in the magnitude of the reflection coefficients of the antenna, receiver, and open cable, which are 0.001, 0.001, and 0.002 (linear), respectively, would result in a 200 mK deviation on the detected signal, and a typical measurement error of 0.48 ° , 0.78 ° , or 0.15 ° in the respective phases would cause a 40 mK deviation. The VNA measurement error can greatly affect the result of a 21 cm global spectrum experiment using this calibration technique, and such a feature could be mistaken for or be combined with the 21 cm signal. [ABSTRACT FROM AUTHOR]

The first step in improving a model is to identify deficiencies in the model forecast. With the continuous advancement of numerical prediction technology, the precise assessment and analysis of model prediction errors, particularly the traceable technology of systematic errors, has gradually become a pivotal issue in model evaluation. Atmospheric energy circulation, as a fundamental principle of atmospheric motion, accurately represents the dynamic and physical interaction mechanisms. With a deeper understanding of the atmospheric energy cycle process, its applications have also expanded continuously. Particularly in recent decades, it has been used to assess the performance of numerical models and reanalysis datasets, serving as an essential metric for understanding model forecast capability and identifying systematic errors. Encompassed within the mixed space-time domain energy cycle are the mean circulation, stationary (deviation from the zonal mean), and transient (deviation from the temporal mean) eddies, and their interconversions of the available potential energy and kinetic energy, with each component holding physical significance. Based on the mixed space-time domain energy cycle framework and scale analysis methods, the energy cycle error characteristics and sources in CMA-GFS at planetary scales (zonal wavenumber 1-3) and synoptic and below (greater than zonal wavenumber 3) scales are examined using CMA-GFS global forecast product and ERA5 global reanalysis data in 2022. Results show that CMA-GFS can effectively replicate characteristics of the atmospheric energy cycle. However, its overestimation of baroclinity results in a stronger available potential energy of the mean circulation, which shows an increasing trend with forecast lead time. The stationary and transient eddy energy are dominated by planetary scales and synoptic and below scales, respectively. Errors in the available potential energy of the stationary eddy component and transient eddy component are determined by thermal conditions. CMA-GFS shows higher stationary eddy available potential energy and less transient eddy available potential energy. Systematic underestimations are observed in kinetics of stationary eddy component and transient eddy component, with predominantly negative errors concentrated near centers of subtropical jets and the polar night jet. This is primarily due to stronger barotropic transports, which transfer more energy from eddies to the mean circulation. As the baroclinity gradually increased, the transient eddy also increased after 120 h lead time. CMA-GFS underestimates four eddy energies in the boreal winter and overestimates or slightly underestimates them in the boreal summer, leading to a significant weakening of their seasonal variation.

Path integration is the navigational process by which animals construct a memory of a previous location by continuously measuring and summing their movements to form a single home vector pointing to the starting location. It is intrinsically error prone, subject to random errors and, potentially, to systematic errors in either measurement or the summing algorithm. Both types of errors lead to an incorrect vector memory and thus to an error in homing. Because the errors are incurred when animals move, they are theoretically predictable from the movements. We analyzed the behavior of fiddler crabs (Leptuca pugilator) as they performed foraging excursions followed by homing with varying degrees of error. From video recordings we measured body orientations and locations and computed these spatiotemporal path characteristics: duration, distance, turns, bearing and arc sector. These were analyzed for their effect on, separately, the magnitude, and the direction, of crabs’ homing error. The magnitude of the homing error was predicted by arc sector, Δbearing and path length, and several interactions. The direction of the homing error was predicted by interactions including arc sector x Δbearing, arc sector x turns, and Δbearing x turns. Covariance among these factors results in a path that traces a large arc while maintaining body orientation toward the burrow direction and leads to an error with the same clockwise/counterclockwise sign as the arc and the body turns. These results place L. pugilator’s path integration mechanism among others with known systematic errors.

Systematic and random errors based on self-reported diet may bias estimates of dietary intake. The objective of this pilot study was to describe errors in self-reported dietary intake by comparing 24 h dietary recalls to provided menu items in a controlled feeding study. This feeding study was a parallel randomized block design consisting of a standard diet (STD; 15% protein, 50% carbohydrate, 35% fat) followed by either a high-fat (HF; 15% protein, 25% carbohydrate, 60% fat) or a high-carbohydrate (HC; 15% protein, 75% carbohydrate, 10% fat) diet. During the intervention, participants reported dietary intake in 24 h recalls. Participants included 12 males (seven HC, five HF) and 12 females (six HC, six HF). The Nutrition Data System for Research was utilized to quantify energy, macronutrients, and serving size of food groups. Statistical analyses assessed differences in 24 h dietary recalls vs. provided menu items, considering intervention type (STD vs. HF vs. HC) (Student’s t-test). Caloric intake was consistent between self-reported intake and provided meals. Participants in the HF diet underreported energy-adjusted dietary fat and participants in the HC diet underreported energy-adjusted dietary carbohydrates. Energy-adjusted protein intake was overreported in each dietary intervention, specifically overreporting beef and poultry. Classifying misreported dietary components can lead to strategies to mitigate self-report errors for accurate dietary assessment.

Introduction: Ultrasound estimated fetal weight is increasingly being used in the monitoring of fetal growth. Large systematic and random errors in estimated fetal weight have been reported; these may have an impact on the accuracy of fetal growth monitoring. The aim of this study was to attempt to evaluate these systematic and random errors by analysis of serial ultrasound data. Methods: Ultrasound measurements and birthweights were retrospectively collected for 100 unselected patients who had undergone serial ultrasound. Birthweights were used to calculate expected fetal growth trajectories using a method for generating growth charts based on customised birthweights. Estimated fetal weight results were then compared with the expected growth trajectories to evaluate systematic and random differences. Results: Incomplete measurement sets were excluded, reducing the number of scans to less than three for 13 subjects. A further 17 subjects with suspected pathological growth trajectories were excluded. The final analysis included 70 subjects with a total of 246 scans. The mean difference between estimated fetal weight and expected weight over three to six scans ranged from −17.5% to 38.3% with a mean of 8.4%, representing the systematic difference. The standard deviation of these differences ranged from 0.4% to 21% with a mean of 4.3%, representing random difference. Conclusion: Systematic and random differences between estimated fetal weight and expected fetal weight are significant and make interpretation of fetal growth difficult. Further improvements to formulae and growth curves are required and audit of fetal measurements is essential to service improvement. [ABSTRACT FROM AUTHOR]

Bias assessment (systematic errors) is fundamental in industry and service laboratories, where reliable results must be obtained to give correct answers to specific problems. Therefore, knowledge and practice in quality methodologies is of fundamental importance for students. Unfortunately, laboratory lessons often focus on connecting theory and practice without developing the quality assurance skills that employers consider to be useful. Here, an external quality methodology based on proficiency testing schemes has been developed to introduce students to the methodologies and skills that are required to detect and find the source of biased results in the laboratory. It has been found that a large percentage of results obtained during laboratory lessons are in fact biased. The new methodology obliges students to practise how to detect biased results and, specially, the critical thinking skills that are needed to reveal the sources of bias in the laboratory. In the case study evaluated here, the application of the proposed methodology allowed students to detect the source of their biased result in 95% of the cases. Errors found were mainly due to personnel errors, as a consequence of deficits in basic hands-on skills and calculation errors. [ABSTRACT FROM AUTHOR]

Simple Summary: Most aesthetic sports where judges subjectively rate performances can suffer from issues like systematic errors due to biases. Equestrian dressage is defined through the intricate interaction between horse and rider. Assessing these interactions can be particularly complex, often exceeding human processing capabilities. The study focuses on whether the current dressage system predisposes international judges to using biases, and inadvertently favours certain horse-rider combinations. The study examined 510 judging scores, gathered from seven elite-level dressage competitions held between May 2022 and April 2023. The effect of different factors, such as whether riders competed in their home country, if they had the same nationality as the judges, their starting order during the competition, and how they were ranked according to previous performances were analysed. Results showed that all these factors influence the final dressage results. In order to assist judges in providing objective, transparent scores, a clear evidence-based set of judging guidelines should be developed, which would prevent judges from having to resort to cognitive short cuts. That way, the complexity of judging is reduced, making scores more objective, transparent and fair. Sport performances judged subjectively often suffer from systematic errors due to biases, with the sport of equestrian dressage being no exception. This study examines whether international dressage judges display systematic errors while evaluating elite horse-rider combinations. Data from seven 5* Grand Prix dressage events between May 2022 and April 2023 were analyzed (510 judges' scores) using Multivariable Linear Regression Analysis. Five predictor variables—Home, Same Nationality, Compatriot, FEI Ranking and Starting Order—were studied in relation to Total Dressage Score (TS). The model accounted for 44.1% of TS variance; FEI Ranking, Starting Order, Compatriot, Same Nationality, and Home were statistically significant (p < 0.001). Judges exhibited nationalistic and patriotism-by-proxy biases, awarding significantly higher scores to riders from their countries (p < 0.001). FEI Ranking and Starting Order also influenced scores significantly (p < 0.001). These biases, combined, created a cascade effect benefiting a specific group of riders. To address this, measures should be taken to develop a more objective judging system that is based on unequivocal, transparent and evidence-based criteria and supports the continuous development of a fair, sustainable, equine welfare orientated sport that fosters societal acceptance, [ABSTRACT FROM AUTHOR]

We propose an in situ self‐calibration method by detecting and matching intensity features on the local planes in overlapping point clouds based on the Förstner operator. We successfully matched the intensity features from scans at different locations by feature matching on common local planes rather than on the rasterised grids of the horizontal and vertical angles adopted by the affirmed keypoint‐based algorithm. The capability of extracting features from different stations offers the possibility of comprehensive scanner calibration, solving the disadvantage that the existing keypoint‐based methods can only estimate the two‐face‐sensitive model parameters. The proposed algorithm has been tested with a high‐precision panoramic scanner, Leica RTC360, using datasets from a calibration hall and a general working scenario. It has been shown that the proposed approach consistently calibrates the two‐face‐sensitive model parameters with the affirmed keypoint‐based one. For the case of comprehensive calibration with the offset estimated and some angular parameters separated where the previous keypoint‐based one failed, the proposed algorithm achieves an accuracy of 0.16 mm, 2.7″ and 2.1″ in range, azimuth and elevation for the estimated target centres. The proposed algorithm can accurately calibrate two‐face‐sensitive and more comprehensive model parameters without any preparation on‐site, for example, mounting targets. [ABSTRACT FROM AUTHOR]

Precipitation and temperature biases from a set of Regional Climate Models from the CORDEX initiative have been analysed to assess the extent to which the biases may impact the climate change signal. The analysis has been performed for the South American CORDEX domain. A large warm bias was found over central Argentina (CARG) for most models, mainly in the summer season. Results indicate that the possible origin of this bias is an overestimation of the incoming shortwave radiation, in agreement with an underestimation of the relative humidity at 850 hPa, a variable that could be used to diagnose cloudiness. Regarding precipitation, the largest biases were found during summertime over northeast of Brazil (NEB), where most models overestimate the precipitation, leading to wet biases over that region. This bias agrees with models' underestimation of both the moisture flux convergence and the relative humidity at lower levels of the atmosphere. This outcome suggests that the generation of more clouds in the models may drive the wet bias over NEB. These systematic errors could affect the climate change signal, considering that these biases may not be stationary. For both CARG and NEB regions, models with higher warm biases project higher warming levels, mainly in the summer season. In addition, it was found that these relationships are statistically significant with a confidence level of 95%, pointing out that biases are linearly linked with the climate change signal. For precipitation, the relationship between the biases and the projected precipitation changes is only statistically significant for the NEB region, where models with the largest wet biases present the greatest positive precipitation changes during the warm season. As in the case of biases, the analysis of the temperature and precipitation projections over some regions of South America suggests that clouds could affect them. The results found in this study point out that the analysis of the bias behaviour could help in a better interpretation of the climate change signal. [ABSTRACT FROM AUTHOR]

The redshifted 21 cm line signal is a powerful probe of the cosmic dawn and the epoch of reionization. The global spectrum can potentially be detected with a single antenna and spectrometer. However, this measurement requires an extremely accurate calibration of the instrument to facilitate the separation of the 21 cm signal from the much brighter foregrounds and possible variations in the instrument response. Understanding how the measurement errors propagate in a realistic instrument system and affect system calibration is the focus of this work. We simulate a 21 cm global spectrum observation based on the noise wave calibration scheme. We focus on how measurement errors in reflection coefficients affect the noise temperature and how typical errors impact the recovery of the 21 cm signal, especially in the frequency domain. Results show that for our example set up, a typical vector network analyzer (VNA) measurement error in the magnitude of the reflection coefficients of the antenna, receiver, and open cable, which are 0.001, 0.001, and 0.002 (linear), respectively, would result in a 200 mK deviation on the detected signal, and a typical measurement error of 0.48°, 0.78°, or 0.15° in the respective phases would cause a 40 mK deviation. The VNA measurement error can greatly affect the result of a 21 cm global spectrum experiment using this calibration technique, and such a feature could be mistaken for or be combined with the 21 cm signal.

Contamination with low‐energy radiation leads to an increased number of weighted residuals being larger in absolute terms than three standard uncertainties. For a Gaussian distribution, these rare events occur only in 0.27% of all cases, which is a small number for small‐ to medium‐sized data sets. The correct detection of rare events – and an adequate correction procedure – thus relies crucially on correct standard uncertainties, which are often not available [Henn (2019), Crystallogr. Rev.25, 83–156]. It is therefore advisable to use additional, more robust, metrics to complement the established ones. These metrics are developed here and applied to reference data sets from two different publications about low‐energy contamination. Other systematic errors were found in the reference data sets. These errors compromise the correction procedures and may lead to under‐ or overcompensation. This can be demonstrated clearly with the new metrics. Empirical correction procedures generally may be compromised or bound to fail in the presence of other systematic errors. The following systematic errors, which were found in the reference data sets, need to be corrected for prior to application of the low‐energy contamination correction procedure: signals of 2λ contamination, extinction, disorder, twinning, and too‐large or too‐low standard uncertainties (this list may not be complete). All five reference data sets of one publication show a common resolution‐dependent systematic error of unknown origin. How this affects the correction procedure can be stated only after elimination of this error. The methodological improvements are verified with data published by other authors. [ABSTRACT FROM AUTHOR]

Due to sensor characteristics, geographical environment, electromagnetic interference, electromagnetic silence, information countermeasures, and other reasons, there may be significant system errors in sensors in multi-sensor tracking systems, resulting in poor track-to-track association (TTTA) effect of the system. In order to solve the problem of TTTA under large system errors, this paper proposes an asynchronous anti-bias TTTA algorithm that utilizes the average distance between the nearest neighbor intervals between tracks. This algorithm proposes a systematic error interval processing method to track coordinates, and then defines the nearest neighbor interval average distance between interval coordinate datasets and interval coordinate points, and then uses grey theory to calculate the correlation degree between tracks. Finally, the Jonker–Volgenant algorithm is combined to use the canonical allocation method for TTTA judgment. The algorithm requires less prior information and does not require error registration. The simulation results show that the algorithm can ensure a high average correct association rate (over 98%) of asynchronous unequal rate tracks under large system errors, and achieve stable association, with good association and anti-bias performance. Compared with other algorithms, the algorithm maintains good performance for different target numbers and processing cycles, and has good superiority and robustness. [ABSTRACT FROM AUTHOR]

Digital speckle photography is a displacement field measurement method that employs laser speckles as surface markers. Since the approach requires only one reference image without a preparation of the sample and provides a fast, single-shot measurement with interferometric precision, the method is applied for in-process measurements in manufacturing engineering. Due to highly localized loads, higher-order displacement gradients occur in manufacturing processes and it is an open research question how these gradients affect the measurement errors of digital speckle photography. We simulate isotropic Gaussian surface topographies, apply a displacement field and then generate laser speckle patterns, which are evaluated with digital image correlation and subsequently the resulting random and systematic errors of the displacement field are analyzed. We found that the random error is proportional to the first-order displacement gradient and results from decorrelation of the laser speckles. The systematic error is mainly caused by the evaluation algorithm and is linearly dependent on the second-order gradient and the subset size. We evaluated in-process displacement measurements of laser hardening, grinding and single-tooth milling where we determined the relative error caused by displacement gradients to be below 2.5% based on the findings from the simulative study. [ABSTRACT FROM AUTHOR]

The stepped micro-mirror imaging Fourier transform spectrometer (SIFTS) has the advantages of high throughput, compactness, and stability. However, the systematic errors in the interference core of the SIFTS have a significant impact on the interferogram and the reconstructed spectrum. In order to reduce the influence of systematic errors, a transfer error model of the systematic errors in the interference core of the SIFTS is established, and an interferogram and spectrum calibration method is presented, which combines the least squares fitting calibration and the row-by-row fast Fourier transform-inverse fast Fourier transform (FFT-IFFT) flat-field calibration. The experimental results show that the methods can sufficiently reduce the influence of systematic errors in the interference core of the SIFTS, such as the interferogram fringe tilt, the peak position shift of the reconstructed spectrum, and the error of spectral response. [ABSTRACT FROM AUTHOR]

Machine learning has been used in hydrological applications for decades, and recently, it was proven to be more efficient than sophisticated physically based modelling techniques. In addition, it has been used in hybrid frameworks that combine hydrological and machine learning models. The concept behind the latter is the use of machine learning as a filter that advances the performance of the hydrological model. In this study, we employed such a hybrid approach but with a different perspective and objective. Machine learning was used as a tool for analyzing the error of hydrological models in an effort to understand the source and the attributes of systematic modelling errors. Three hydrological models were applied to three different case studies. The results of these models were analyzed with a recurrent neural network and with the k-nearest neighbours algorithm. Most of the systematic errors were detected, but certain types of errors, including conditional systematic errors, passed unnoticed, leading to an overestimation of the confidence of some erroneously simulated values. This is an issue that needs to be considered when using machine learning as a filter in hybrid networks. The effect of conditional systematic errors can be reduced by naively combining the simulations (mean values) of two or more hydrological models. This simple technique reduces the magnitude of conditional systematic errors and makes them more discoverable to machine learning models. [ABSTRACT FROM AUTHOR]

Stereo digital image correlation (Stereo-DIC) is recurrent in photo-mechanics to measure kinematic fields which can be of high interest for instrumenting open-mould forming processes. Nevertheless, in the presence of pre-heating operations, as observed in the context of thermoforming processes, natural convective heat flows risk emerging and causing optical distortions in the recorded images. Consequently, this alters the precision of the measured full-fields of displacements. To address these challenges, this study proposes an experimental approach with two distinctive features. Firstly, it focuses on regenerating the heat haze effect at a laboratory scale within a partially opened vertical enclosure and without utilizing any filtering air flows. Secondly, the study quantifies the spatial and temporal variations of errors through statistical analyses of the differences between measurements obtained from quasi-static speckle translations and known imposed displacements. Experimental results indicate that the main cause of displacement errors is related to the 3D nature of the hot air turbulence caused by the natural convection phenomenon. This observation is supported by the detection of feather-shaped heat flows causing optical out-of-plane surface deviations. Furthermore, the study validates the possibility of obtaining time-dependent corrective functions for bias errors, which characterize the performance of the calibrated Stereo-DIC system in the presence of heat haze. Despite the limitation of extensive measurements required by the proposed approach, this study contributes to addressing the heat haze effect and constitutes a step towards extending the use of stereo-DIC for in-situ instrumentation of short-duration thermomechanical tests in the presence of heat haze. [ABSTRACT FROM AUTHOR]

Using multiple ion beam analysis measurements or techniques combined with self-consistent data processing generally allows for extracting more (or more accurate) information than processing data from single measurements separately. Solving ambiguities, improving the final depth resolution, defining constraints and extending applicability are the main strengths of the data-fusion approach, which essentially consists in formulating a multi-objective minimization problem that can be tackled by the adoption of the weighted-sum method. A simulation study is reported in order to evaluate the systematic error inserted in the analysis by the choice of a specific objective function, or even by the weights or normalization adopted in the weighted-sum method. We demonstrate that the bias of the analysed objective functions asymptotically converges to the true value for better statistics and that the measurement can be ranked by its information content, where some combinations of measurements better constrain the multi-objective optimization. [ABSTRACT FROM AUTHOR]

Sport performances judged subjectively often suffer from systematic errors due to biases, with the sport of equestrian dressage being no exception. This study examines whether international dressage judges display systematic errors while evaluating elite horse-rider combinations. Data from seven 5* Grand Prix dressage events between May 2022 and April 2023 were analyzed (510 judges’ scores) using Multivariable Linear Regression Analysis. Five predictor variables—Home, Same Nationality, Compatriot, FEI Ranking and Starting Order—were studied in relation to Total Dressage Score (TS). The model accounted for 44.1% of TS variance; FEI Ranking, Starting Order, Compatriot, Same Nationality, and Home were statistically significant (p < 0.001). Judges exhibited nationalistic and patriotism-by-proxy biases, awarding significantly higher scores to riders from their countries (p < 0.001). FEI Ranking and Starting Order also influenced scores significantly (p < 0.001). These biases, combined, created a cascade effect benefiting a specific group of riders. To address this, measures should be taken to develop a more objective judging system that is based on unequivocal, transparent and evidence-based criteria and supports the continuous development of a fair, sustainable, equine welfare orientated sport that fosters societal acceptance,

The development of fully coherent free electron lasers and diffraction limited storage ring x-ray sources has brought to focus the need for higher performing x-ray optics with unprecedented tolerances for surface slope and height errors and roughness. For example, the proposed beamlines for the future upgraded Advance Light Source, ALS-U, require optical elements characterized by a residual slope error of

This paper presents a polarization calibration method applied to a microwave polarimeter demonstrator based on a near-infrared (NIR) frequency up-conversion stage that allows both optical correlation and signal detection at a wavelength of 1550 nm. The instrument was designed to measure the polarization of cosmic microwave background (CMB) radiation from the sky, obtaining the Stokes parameters of the incoming signal simultaneously, in a frequency range from 10 to 20 GHz. A linearly polarized input signal with a variable polarization angle is used as excitation in the polarimeter calibration setup mounted in the laboratory. The polarimeter systematic errors can be corrected with the proposed calibration procedure, achieving high levels of polarization efficiency (low polarization percentage errors) and low polarization angle errors. The calibration method is based on the fitting of polarization errors by means of sinusoidal functions composed of additive or multiplicative terms. The accuracy of the fitting increases with the number of terms in such a way that the typical error levels required in low-frequency CMB experiments can be achieved with only a few terms in the fitting functions. On the other hand, assuming that the calibration signal is known with the required accuracy, additional terms can be calculated to reach the error levels needed in ultrasensitive B-mode polarization CMB experiments. [ABSTRACT FROM AUTHOR]

Data obtained from long-term systematic measurements of thermal stratification in the atmospheric boundary layer (ABL) over the Moscow metropolis and simulation data obtained using a high-resolution mesoscale model have been compared. Model results demonstrate there to be a more stable stratification (when compared to observational data) at night in winter. Under conditions of unstable stratification, even in winter, models dampen inhomogeneities that take place too rapidly, which leads to an underestimation of the spectrum of mesoscale fluctuations, average gradients, and their dynamics in the lower part of the ABL in the models. A simple algorithm for estimating errors in numerical forecasts is proposed and it is shown that errors in near-surface temperature forecasts are mainly associated with the simulation of processes occurring in the ABL. It is also shown that microwave radiometers operating within a band of 60 GHz may be a simple and reliable tool for estimating the accuracy of boundary-layer parameterizations in numerical weather-forecast models. [ABSTRACT FROM AUTHOR]

When the total least squares (TLS) solution is used to solve the parameters in the errors-in-variables (EIV) model, the obtained parameter estimations will be unreliable in the observations containing systematic errors. To solve this problem, we propose to add the nonparametric part (systematic errors) to the partial EIV model, and build the partial EIV model to weaken the influence of systematic errors. Then, having rewritten the model as a nonlinear model, we derive the formula of parameter estimations based on the penalized total least squares criterion. Furthermore, based on the second-order approximation method of precision estimation, we derive the second-order bias and covariance of parameter estimations and calculate the mean square error (MSE). Aiming at the selection of the smoothing factor, we propose to use the U curve method. The experiments show that the proposed method can mitigate the influence of systematic errors to a certain extent compared with the traditional method and get more reliable parameter estimations and its precision information, which validates the feasibility and effectiveness of the proposed method.

The main problem that arises in the mobility spectrum analysis (MSA) is the appearance of additional spectral lines, which are not related to real physical processes and transport mechanisms. The paper proposes a simple model of systematic errors occurring in magneto-transport measurements and investigates their impact on this adverse phenomenon. The analysis was carried out for typical mobility spectra characteristic of single- and dual-carrier transport. On the basis of numerical experiments, heuristic rules were formulated that can be helpful in distinguishing real spectral lines from false peaks caused by measurement errors. • A simple model of systematic errors in transport measurements has been proposed. • The impact of these errors on the mobility spectra has been investigated. • In the case of two-carrier transport, one additional spectral line always shows up. • Such artificial (ghost) peak has unique properties that allow it to be identified. [ABSTRACT FROM AUTHOR]

In a prospective cohort of 240 paraoptic tumors patients treated with protons, there was 10° inter-individual gaze angle variability (up to 30°). In a random 21-patient subset with initial CTs versus and adaptive CTs, 6 (28.57 %) patients had at least twice a 10°-difference in their gaze angle, with > 5 Gy difference on the retina/macula or papilla in 2/21 (9.52 %) and 1/21 (4.76 %) based on cumulative dose from rescans, respectively. [ABSTRACT FROM AUTHOR]

All surveying measurements take place in an environment which affects the results obtained through its physical properties. Many of these effects are reflected in the results as random or systematic errors, which reduce the measured variable's precision. Tripod instability during measurements can be observed due to deflection of the surveying instrument's level during measurements, or after finishing the measurements at the given position. Height changes in the instrument can be caused by settling (lowering) or lifting (raising) the tripod's legs under the action of the soil's elastic properties. Alternatively, the tripod may be deformed due to changes in the physical parameters of the environment. This study aimed to determine the size of instrument height changes resulting from all three tripod leg movements in a vertical direction. The magnitude of the difference in the instrument's horizontal line depends mainly on the soil's mechanical properties and the time required for the operation at the given position. The rate of height change was not constant. Tripod height increase was stabilized after about 15 min at approx. 0.03 mm (in summer) and 0.05 mm (in autumn). In winter, the height of the tripod increased by only 0.01 mm. Statistical testing of the experiment's resulting design revealed a significant effect of soil moisture, temperature, and type on changes in the leveling instrument's horizontal axis. [ABSTRACT FROM AUTHOR]

We develop an anomaly detection method when systematic anomalies, possibly statistically very similar to genuine inputs, are affecting control systems at the input and/or output stages. The method allows anomaly free inputs (i.e., those before contamination) to originate from a wide class of random sequences, thus opening up possibilities for diverse applications. To illustrate how the method works on data, and how to interpret its results and make decisions, we analyze several actual time series, which are originally nonstationary but in the process of analysis are converted into stationary. As a further illustration, we provide a controlled experiment with anomaly free inputs following an ARMA time series model under various contamination scenarios. [ABSTRACT FROM AUTHOR]

Accurate camera calibration is a precondition for many computer vision applications. Calibration errors, such as wrong model assumptions or imprecise parameter estimation, can deteriorate a system's overall performance, making the reliable detection and quantification of these errors critical. In this work, we introduce an evaluation scheme to capture the fundamental error sources in camera calibration: systematic errors (biases) and uncertainty (variance). The proposed bias detection method uncovers smallest systematic errors and thereby reveals imperfections of the calibration setup and provides the basis for camera model selection. A novel resampling-based uncertainty estimator enables uncertainty estimation under non-ideal conditions and thereby extends the classical covariance estimator. Furthermore, we derive a simple uncertainty metric that is independent of the camera model. In combination, the proposed methods can be used to assess the accuracy of individual calibrations, but also to benchmark new calibration algorithms, camera models, or calibration setups. We evaluate the proposed methods with simulations and real cameras. [ABSTRACT FROM AUTHOR]

The laser tracker is a widely used instrument in many industrial and metrological applications with high demand measurement accuracy. Imperfections in construction and misalignment of individual parts deliver systematic errors in the measurement results. All error sources need to be identified and reduced to the minimum to achieve the best possible accuracy. The paper summarizes error sources of the laser tracker without beam steering mirror with emphasis on error modeling. Descriptions of error models are provided for the static and kinematic type of measurement. [ABSTRACT FROM AUTHOR]

Introduction: Radiation therapy involves a multistep procedure; therefore, the error in patient set up is an inherent part of the treatment. Main purpose of this study was to determine the clinical target volume (CTV) to planning target volume (PTV) in head and neck cancer patients. Material and Methods: A total of 15 patients who had daily portal images during the treatment courses were randomly selected in the present study. Systematic (Σ) and random (σ) errors were evaluated in three directions. The Isogray treatment planning system and Elekta linear accelerator were used in this study. Moreover, we had used MOSIAQ software as arecord and Verify system. Setup margins were calculated using three published margin recipes, including the International Commission on Radiation Units and Measurements (ICRU) report 62, as well as Stroom’s and van Herk’s formulae. Results: Average magnitude of the translational errors was reported between 0.7 and 10 mm. The systematic and random errors for head and neck cancer patients were 3.55 (2.58-4.52) and 1.83 (1.56-2.10) mm, respectively. According to the ICRU report 62, as well as Stoorm’s and van Herk’s formulas, the required margins to cover the target were obtained within the ranges of 3.1-4.9, 6.4-10.5, and 7.7-12.7 mm, respectively. Conclusion: According to the results of the present study, 6.5-10.5 mm extension in CTV to PTV margin can ensure that 90% of the head and neck cancer patients will receive a minimum cumulative CTV dose higher than or equal to 95% of the prescribed dose.

Machine learning has been used in hydrological applications for decades, and recently, it was proven to be more efficient than sophisticated physically based modelling techniques. In addition, it has been used in hybrid frameworks that combine hydrological and machine learning models. The concept behind the latter is the use of machine learning as a filter that advances the performance of the hydrological model. In this study, we employed such a hybrid approach but with a different perspective and objective. Machine learning was used as a tool for analyzing the error of hydrological models in an effort to understand the source and the attributes of systematic modelling errors. Three hydrological models were applied to three different case studies. The results of these models were analyzed with a recurrent neural network and with the k-nearest neighbours algorithm. Most of the systematic errors were detected, but certain types of errors, including conditional systematic errors, passed unnoticed, leading to an overestimation of the confidence of some erroneously simulated values. This is an issue that needs to be considered when using machine learning as a filter in hybrid networks. The effect of conditional systematic errors can be reduced by naively combining the simulations (mean values) of two or more hydrological models. This simple technique reduces the magnitude of conditional systematic errors and makes them more discoverable to machine learning models.

The stepped micro-mirror imaging Fourier transform spectrometer (SIFTS) has the advantages of high throughput, compactness, and stability. However, the systematic errors in the interference core of the SIFTS have a significant impact on the interferogram and the reconstructed spectrum. In order to reduce the influence of systematic errors, a transfer error model of the systematic errors in the interference core of the SIFTS is established, and an interferogram and spectrum calibration method is presented, which combines the least squares fitting calibration and the row-by-row fast Fourier transform-inverse fast Fourier transform (FFT-IFFT) flat-field calibration. The experimental results show that the methods can sufficiently reduce the influence of systematic errors in the interference core of the SIFTS, such as the interferogram fringe tilt, the peak position shift of the reconstructed spectrum, and the error of spectral response.

Purpose: In radiotherapy (RT), the planning CT (pCT) is commonly used to plan the full RT‐course. Due to organ deformation and motion, the organ shapes seen at the pCT will not be identical to their shapes during RT. Any difference between the pCT organ shape and the organ's mean shape during RT will cause systematic errors. We propose to use statistical shrinkage estimation to reduce this error using only the pCT and the population mean shape computed from training data. Methods: The method was evaluated for the rectum in a cohort of 37 prostate cancer patients that had a pCT and 7–10 treatment CTs with rectum delineations. Deformable registration was performed both within‐patient and between patients, resulting in point‐to‐point correspondence between all rectum shapes, which enabled us to compute a population mean rectum. Shrinkage estimates were found by combining the pCTs linearly with the population mean. The method was trained and evaluated using leave‐one‐out cross validation. The shrinkage estimates and the patient mean shapes were compared geometrically using the Dice similarity index (DSI), Hausdorff distance (HD), and bidirectional local distance. Clinical dose/volume histograms, equivalent uniform dose (EUD) and minimum dose to the hottest 5% volume (D5%) were compared for the shrinkage estimate and the pCT. Results: The method resulted in moderate but statistically significant increase in similarity to the patient mean shape over the pCT. On average, the HD was reduced from 15.6 to 13.4 mm, while the DSI was increased from 0.74 to 0.78. Significant reduction in the bias of volume estimates was found in the DVH‐range of 52.5–65 Gy, where the bias was reduced from −1.3 to −0.2 percentage points, but no significant improvement was found in EUD or D5%, Conclusions: The results suggest that shrinkage estimation can reduce systematic errors due to organ deformations in RT. The method has potential to increase the accuracy in RT of deformable organs and can improve motion modeling. [ABSTRACT FROM AUTHOR]

In developing a research protocol, authors must consider the possible errors that may occur throughout the study. In clinical research, two types of biases are recognized: random errors and systematic errors; the latter are called biases. To date, dozens of biases have been described, which is why the purpose of this article is to describe the main biases that can occur in clinical research studies, as well as strategies to avoid them or to minimize their effects. Since there are several classifications, in order to provide a more practical overview in this review, the biases are grouped into three types: selection biases, information (or performance) biases, and confounding biases. In addition, to make it even more specific, we describe the biases considering the purpose of the research: prognosis, therapeutics, causality, and diagnostic test studies. [ABSTRACT FROM AUTHOR]

The Satellite Laser Ranging (SLR) technique provides very accurate distance measurements to artificial Earth satellites. SLR is employed for the realization of the origin and the scale of the terrestrial reference frame. Despite the high precision, SLR observations can be affected by various systematic errors. So far, range biases were used to account for systematic measurement errors and mismodeling effects in SLR. Range biases are constant for all elevation angles and independent of the measured distance to a satellite. Recently, intensity-dependent biases for single-photon SLR detectors and offsets of barometer readings and meteorological devices were reported for some SLR stations. In this paper, we study the possibility of the direct estimation of tropospheric biases from SLR observations to LAGEOS satellites. We discuss the correlations between the station heights, range biases, tropospheric biases, and their impact on the repeatability of station coordinates, geocenter motion, and the global scale of the reference frame. We found that the solution with the estimation of tropospheric biases provides more stable station coordinates than the solution with the estimation of range biases. From the common estimation of range and tropospheric biases, we found that most of the systematic effects at SLR stations are better absorbed by elevation-dependent tropospheric biases than range biases which overestimate the total bias effect. The estimation of tropospheric biases changes the SLR-derived global scale by 0.3 mm and the geocenter coordinates by 1 mm for the Z component, causing thus an offset in the realization of the reference frame origin. Estimation of range biases introduces an offset in some SLR-derived low-degree spherical harmonics of the Earth’s gravity field. Therefore, considering elevation-dependent tropospheric and intensity biases is essential for deriving high-accuracy geodetic parameters. [ABSTRACT FROM AUTHOR]